An Efficient Hybrid Deep Learning Framework for Predicting Student Academic Performance
DOI:
https://doi.org/10.56294/sctconf2024759Keywords:
Student, Academic Performance, Educational Data Mining (EDM), Convolutional Neural Networks (CNNs)Abstract
Introduction: educational data analysis with data mining techniques for enhanced learning is increasing. Voluminous data available through institutions, online educational resources and virtual educational courses could be useful in tracking learning patterns of students. Data mining techniques could be helpful for predicting students’ academic performance from raw data. Conventional Machine Learning (ML) techniques have so far been widely used for predicting this.
Methods: however, research available on the Convolutional Neural Networks (CNNs) architecture is very scarce in the context of the academic domain. Therefore, in this work a hybrid CNN model involving 2 different CNN models for forecasting academic performance. The one-dimensional data is converted into two-dimensional equivalent to determine efficiency of the hybrid model which is subsequently compared with many existing.
Result: the experimental results are evaluated using various performance metrics like precision, accuracy, recall and F-Score.
Conclusion: the proposed hybrid model outperforms-Nearest Neighbour (K-NN), Decision Trees (DTs), and Artificial Neural Network (ANN) in terms of precision, accuracy, recall and F-Score
References
1. Kaunang FJ, and Rotikan R. Students' academic performance prediction using data mining. In Third International Conference on Informatics and Computing (ICIC), pp. 1-5. https://doi.org/10.1109/IAC.2018.8780547.
2. Saravanan V, and Shanmuga Priya N. Feature Detection and Extraction Techniques for Real‐Time Student Monitoring in Sensor Data Environments. Sensor Data Analysis and Management: The Role of Deep Learning, pp. 97-102. https://doi.org/10.1002/9781119682806.ch5.
3. Ha DT, Loan PTT, Giap CN, and Huong NTL. An empirical study for student academic performance prediction using machine learning techniques. International Journal of Computer Science and Information Security (IJCSIS), 18(3), pp. 75-82.
4. Asiah M, Zulkarnaen KN, Safaai D, Hafzan MYNN, Saberi MM, and Syuhaida SS. A review on predictive modeling technique for student academic performance monitoring. In MATEC Web of Conferences, 255, pp. 1-8. https://doi.org/10.1051/matecconf/201925503004.
5. Nuankaew W, and Thongkam J. Improving student academic performance prediction models using feature selection. In 17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), pp. 392-395. https://doi.org/10.1109/ECTI-CON49241.2020.9158286.
6. Li X, Zhu X, Zhu X, Ji Y, and Tang X. Student academic performance prediction using deep multi-source behavior sequential network. In Advances in Knowledge Discovery and Data Mining: 24th Pacific-Asia Conference, PAKDD, Proceedings, Part I 24, pp. 567-579. https://doi.org/10.1007/978-3-030-47426-3_44.
7. Lu OH, Huang AY, Huang JC, Lin AJ, Ogata H, and Yang SJ. Applying learning analytics for the early prediction of Students' academic performance in blended learning. Journal of Educational Technology & Society, 21(2), pp. 220-232.
8. Jiao P, Ouyang F, Zhang Q, and Alavi AH. Artificial intelligence-enabled prediction model of student academic performance in online engineering education. Artificial Intelligence Review, 55(8), pp. 6321-6344. https://doi.org/10.1007/s10462-022-10155-y.
9. Huang S, and Fang N. Predicting student academic performance in an engineering dynamics course: A comparison of four types of predictive mathematical models. Computers & Education, 61, pp. 133-145. https://doi.org/10.1016/j.compedu.2012.08.015.
10. Nabil A, Seyam M, and Abou-Elfetouh A. Prediction of students’ academic performance based on courses’ grades using deep neural networks. IEEE Access, 9, pp. 140731-140746. https://doi.org/10.1109/ACCESS.2021.3119596.
11. Kamal P, and Ahuja S. Academic performance prediction using data mining techniques: Identification of influential factors effecting the academic performance in undergrad professional course. In Harmony Search and Nature Inspired Optimization Algorithms: Theory and Applications, ICHSA, pp. 835-843. https://doi.org/10.1007/978-981-13-0761-4_79.
12. Rebai S, Yahia FB, and Essid H. A graphically based machine learning approach to predict secondary schools performance in Tunisia. Socio-Economic Planning Sciences, 70, pp. 100724. https://doi.org/10.1016/j.seps.2019.06.009.
13. Trakunphutthirak R, and Lee VC. Application of educational data mining approach for student academic performance prediction using progressive temporal data. Journal of Educational Computing Research, 60(3), pp. 742-776. https://doi.org/10.1177/07356331211048777.
14. Helal S, Li J, Liu L, Ebrahimie E, Dawson S, Murray DJ, and Long Q. Predicting academic performance by considering student heterogeneity. Knowledge-Based Systems, 161, pp. 134-146. https://doi.org/10.1016/j.knosys.2018.07.042.
15. Francis BK, and Babu SS. Predicting academic performance of students using a hybrid data mining approach. Journal of medical systems, 43(6), pp. 1-15. https://doi.org/10.1007/s10916-019-1295-4.
16. Beaulac C, and Rosenthal JS. Predicting university students’ academic success and major using random forests. Research in Higher Education, 60, pp. 1048-1064. https://doi.org/10.1007/s11162-019-09546-y.
17. Boyer SSA. Transfer learning for predictive models in MOOCs (Doctoral dissertation, Massachusetts Institute of Technology).
18. Rivas A, Gonzalez-Briones A, Hernandez G, Prieto J, and Chamoso P. Artificial neural network analysis of the academic performance of students in virtual learning environments. Neurocomputing, 423, pp. 713-720. https://doi.org/10.1016/j.neucom.2020.02.125.
19. Yousafzai BK, Khan SA, Rahman T, Khan I, Ullah I, Ur Rehman A, Baz M, Hamam H, and Cheikhrouhou O. Student-performulator: student academic performance using hybrid deep neural network. Sustainability, 13(17), pp. 1-21. https://doi.org/10.3390/su13179775.
20. Dabhade P, Agarwal R, Alameen KP, Fathima AT, Sridharan R, and Gopakumar G. Educational data mining for predicting students’ academic performance using machine learning algorithms. Materials Today: Proceedings, 47, pp. 5260-5267. https://doi.org/10.1016/j.matpr.2021.05.646.
21. Baashar Y, Alkawsi G, Mustafa A, Alkahtani AA, Alsariera YA, Ali AQ, Hashim W, and Tiong SK. Toward predicting student’s academic performance using artificial neural networks (ANNs). Applied Sciences, 12(3), pp. 1-16. https://doi.org/10.3390/app12031289.
22. Imran M, Latif S, Mehmood D, and Shah MS. Student academic performance prediction using supervised learning techniques. International Journal of Emerging Technologies in Learning, 14(14), pp. 92-104.
23. Maheswari K, Priya A, Balamurugan A, and Ramkumar S. WITHDRAWN: Analyzing student performance factors using KNN algorithm. https://doi.org/10.1016/j.matpr.2020.12.1024.
24. Hasan R, Palaniappan S, Raziff ARA, Mahmood S, and Sarker KU. Student academic performance prediction by using decision tree algorithm. In 4th international conference on computer and information sciences (ICCOINS), pp. 1-5. https://doi.org/10.1109/ICCOINS.2018.8510600.
25. Rivas A, Gonzalez-Briones A, Hernandez G, Prieto J, and Chamoso P. Artificial neural network analysis of the academic performance of students in virtual learning environments. Neurocomputing, 423, pp. 713-720. https://doi.org/10.1016/j.neucom.2020.02.125.
26. Kuzilek J, Hlosta M, and Zdrahal Z. Open university learning analytics dataset. Scientific data, 4(1), pp. 1-8.
Published
Issue
Section
License
Copyright (c) 2024 M. Viveka, Dr. N.Shanmuga Priya (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.
